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Sentiment analysis and keyphrase extraction / Mahmoud Nabil Mahmoud ; Supervised Amir F. Atiya , Mohamed Aly

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mahmoud Nabil Mahmoud , 2016Description: 71 P. : charts , facsimiles ; 30cmOther title:
  • {uFE97}{uFEA4}{uFEE0}{uFBFF}{uFEDE} ا{uئإؤئ}{uئإأأ}{uئإإإ}ا{uئإأ٣}{uئإؤ٢} و ا{uئإآ٣}{uئإ٩٨}{uئإء٨}{uئإءإ}اج {uئإؤآ}{uئإإ٠}{uئإإ٤}{uئإ٨إ}ت ا{uئإؤئ}{uئإ٩٢}{uئإء٤}{uئإ٩ء} [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: Millions of mutual posts on social media websites every day. This calls for tools to mine social data and extract useful information out of it. Towards this end, this work focuses on four tasks (a)introducing some datasets that can be used for sentiment analysis for Arabic language; (b)performing a sequence of benchmark experiments on each dataset alongside with a method for extracting sentiment lexicons. (c)introducing a deep-learning recurrent neural model for sentiment analysis tested on several SemEval datasets; (d)introducing some new methods for extracting keyphrases from Arabic documents
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Ma.S (Browse shelf(Opens below)) Not for loan 01010110070543000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Ma.S (Browse shelf(Opens below)) 70543.CD Not for loan 01020110070543000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Millions of mutual posts on social media websites every day. This calls for tools to mine social data and extract useful information out of it. Towards this end, this work focuses on four tasks (a)introducing some datasets that can be used for sentiment analysis for Arabic language; (b)performing a sequence of benchmark experiments on each dataset alongside with a method for extracting sentiment lexicons. (c)introducing a deep-learning recurrent neural model for sentiment analysis tested on several SemEval datasets; (d)introducing some new methods for extracting keyphrases from Arabic documents

Issued also as CD

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